US 2013/0238638 by Doron et al. describes a system and method which identify structures within a presentation and detect undesired content in those structures. A decision is made whether to remove portions of the presentation containing the undesired content or the entire presentation, based on determining the domination of the undesired content within the structures of the presentation.
US 2012/0141017 by Krupka et al. describes training set for a post-filter classifier is created from the output of a face detector. The face detector can be a Viola Jones face detector. Face detectors produce false positives and true positives. The regions in the training set are labeled so that false positives are labeled negative and true positives are labeled positive. The labeled training set is used to train a post-filter classifier. The post-filter classifier can be an SVM (Support Vector Machine). The trained face detection classifier is placed at the end of a face detection pipeline comprising a face detector, one or more feature extractors and the trained post-filter classifier. The post-filter reduces the number of false positives in the face detector output while keeping the number of true positives almost unchanged using face detection features different from the Haar features used by the face detector.
Lietz et al. (Adv. Rad. Sci. (2013) 11:101-105) describes pedestrian detection systems which use video scenes as an input.